Authors:
(1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran;
(2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran;
(3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada
(4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania.
Table of Links
RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI
RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI
Conclusion, Limitations, and References
RQ 1: AI and scientometrics
In scientometrics, AI can provide several specific benefits including Publication Analysis, Citation Analysis, Prediction of Research Impact, Collaboration Analysis, Research Trend Analysis, and Knowledge Mapping. The AI benefits in such six subfields (Figure 2) have been discussed, e.g., in [21-31].
These 12 studies demonstrate the potential benefits and strategies for utilizing AI capabilities in scientometrics. How AI can improve the quality, accessibility, and data collection processes in scientometric analyses is further highlighted in Table 1.
The main point is that AI algorithms can analyze large volumes of scientific publications and extract valuable information, such as author and co-author names, affiliations, keywords, and citations [21, 22]. As a result, researchers can gain insight into publication patterns, research networks, and collaborations within a particular scientific field [32, 33].
Moreover, AI algorithms can analyze citation networks to identify the impact and influence of scientific papers, as well as the relationships between different research works [22, 24, 31]. Researchers can use this method to identify highly cited and influential papers, - even sleeping beauties [34] as well as to understand the dynamics of scientific knowledge dissemination.
Interestingly, AI techniques can be employed to predict the impact of scientific research based on various factors, such as author reputation, journal quality, and citation patterns [27]. Analyzing historical data allows AI models to provide insights into the potential impact of research, enabling researchers and institutions to determine the best course of action.
Co-authorship networks can be analyzed by AI to identify and understand research collaborations [28, 30]. By analyzing publication history, author affiliations, and co-authorship patterns, AI can help researchers identify potential collaborators and research networks, enabling better collaboration and knowledge exchange.
In order to identify emerging research trends and topics, AI can analyze large-scale scientific literature [23, 26, 35] For example, by utilizing natural language processing techniques, AI algorithms can automatically extract keywords, topics, and trends from scientific publications, helping researchers identify new research directions and stay up-to-date with the latest advancements in their field.
“Finally”, AI can map the scientific knowledge landscape by analyzing the relationships between different scientific papers, keywords, and concepts [25, 29]. In addition to facilitating literature reviews, hypothesis generation, and research planning, this visualization allows researchers to visualize and understand the structure and evolution of knowledge within a specific research area.
This paper is available on arxiv under CC BY 4.0 DEED license.